Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions

Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future. It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities. Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community\u27s desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications

Organisation of foraging in ants

In social insects, foraging is often cooperative, and so requires considerable organisation. In most ants, organisation is a bottom-up process where decisions taken by individuals result in emergent colony level patterns. Individuals base their decisions on their internal state, their past experience, and their environment. By depositing trail pheromones, for example, ants can alter the environment, and thus affect the behaviour of their nestmates. The development of emergent patterns depends on both how individuals affect the environment, and how they react to changes in the environment. Chapters 4 – 9 investigate the role of trail pheromones and route memory in the ant Lasius niger. Route memories can form rapidly and be followed accurately, and when route memories and trail pheromones contradict each other, ants overwhelmingly follow route memories (chapter 4). Route memories and trail pheromones can also interact synergistically, allowing ants to forage faster without sacrificing accuracy (chapter 5). Home range markings also interact with other information sources to affect ant behaviour (chapter 6). Trail pheromones assist experienced ants when facing complex, difficult-to-learn routes (chapter 7). When facing complicated routes, ants deposit more pheromone to assist in navigation and learning (chapter 7). Deposition of trail pheromones is suppressed by ants leaving a marked path (chapter 5), strong pheromone trails (chapter 7) and trail crowding (chapter 8). Colony level ‘decisions’ can be driven by factors other than trail pheromones, such as overcrowding at a food source (chapter 9). Chapter 10 reviews the many roles of trail pheromones in ants. Chapters 11 – 14 focus on the organisation of cooperative food retrieval. Pheidole oxyops workers arrange themselves non-randomly around items to increase transport speeds (chapter 11). Groups of ants will rotate food items to reduce drag (chapter 12). Chapters 13 and 14 encompass the ecology of cooperative transport, and how it has shaped trail pheromone recruitment in P. oxyops and Paratrechina longicornis. Lastly, chapter 15 provide a comprehensive review of cooperative transport in ants and elsewhere

Fostering values in organizations

Today, values hold a prominent place both in business ethics and in organization theory. However, there persists considerable confusion about what these values are and what role they play in these theories and, therefore, how they can be developed both within the individual and within the organization. Therefore, this paper seeks to define a conception of values based on a theory of human action that can provide a basis for an organization theory, and to propose a series of ideas about how personal and organizational values can be fostered.action theory; motives; organization; strategy; values; virtues;

Doping in Sport: A Behavioural Economics Perspective

This thesis primarily aims to provide a solid theoretical understanding behind the incentive structures, decision making and rationality of athletes who decide to utilize doping decisions within a competitive sporting contest. This thesis analyzes the rationality behind eliciting a doping decision, outline a two-stage model of doping in sport in which athletes choose how much to dope and then how much effort to exert, with payoffs determined by an all-pay auction. We also show that a winner-takes-all prize structure leads to maximum effort (when effort can be monitored) but also maximum cheating when it cannot and explore the complimentary idea that people behave more dishonestly in a sporting environment than they do in other environments through theoretical and experimental analysis

Integration of social and economic information drives cooperation in a collective decision making task.

Social decision-making presents arguably the most complex problem an animal can face. Collective, economic decision-making requires the integration of predictions based on the outcomes of prior interactions alongside predictions generated from ongoing social information. Many economic decisions are made as individuals interact with each other, however how the manner in which animals perceive and display social information affects economic decisions remains largely overlooked. Hence we developed a social dilemma task, traditionally focused on how experienced outcomes affect choices, but allow each rat player access to proximate social information.(...

A systematic literature review of multi-agent pathfinding for maze research

Multi-agent Pathfinding, also known as MAPF, is an Artificial Intelligence problem-solving. The aim is to direct each agent to find its path to reach its target, both individually and in groups. Of course, this path allows agents to move without colliding with each other. This MAPF application is implemented in many areas that require the movement of various agents, such as warehouse robots, autonomous cars, video games, traffic control, Unmanned Aerial Vehicles (UAV), Search and Rescue (SAR), many others. The use of multi-agent in exploring often assumes all areas to be explored are free of obstructions. However, the use of MAPF to achieve their goals often faces static barriers, and even other agents can also be considered dynamic barriers. Because it requires some constraints in the program, such as agents cannot collide with each other. The use of single-agent can find the shortest path through exploration. Still, multi-agent cooperation should shorten the time to find a target location, especially if there is more than one target. This paper explains the Systematic Literature Review (SLR) method to review research on various multi-agent pathfinding. The contribution of this paper is the analysis of multi-agent pathfinding and its potential application in solving maze problems based on an SLR